Expired75% confidenceFactExact time
Providing 3–5 high-quality examples (Few-shot Learning) can generally significantly improve the model's instruction-following accuracy.
5
Sources
75%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
AI Batch Rename Tool: One-Click File Name Standardization with LLM Semantic Understanding
bilibili大飞哥软件自修室6/1/2026
Related Claims
VerifiedFew-shot prompting activates in-context learning capabilities by providing examples.74% similarVerifiedFew-Shot Learning refers to providing a small number of examples (typically 2–8) in the prompt, allowing the model to complete new tasks through analogical reasoning.74% similarUnverified小模型通过知识蒸馏通常比直接在标注数据上训练精度高出2-5个百分点74% similarUnverified小样本学习（Few-Shot Learning）的主流方法包括基于度量学习的Siamese Network、基于元学习的MAML，以及结合预训练大模型的提示微调74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/44752API
curl https://kongchang.com/api/v1/knowledge/claims/44752MCP
get_claim(id=44752)